On the Convergence of Fitting Algorithms in Computer Vision

Authors:
N. Chernov
Affiliations:
Department of Mathematics, University of Alabama at Birmingham, Birmingham 35294
Venue:
Journal of Mathematical Imaging and Vision
Year:
2007

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Abstract

We investigate several numerical schemes for estimating parameters in computer vision problems: HEIV, FNS, renormalization method, and others. We prove mathematically that these algorithms converge rapidly, provided the noise is small. In fact, in just 1-2 iterations they achieve maximum possible statistical accuracy. Our results are supported by a numerical experiment. We also discuss the performance of these algorithms when the noise increases and/or outliers are present.